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• A METHOD OF CONTROLLING A MOBILE ROBOT USING NATURAL LANGUAGE SEMANTICS

  D.S. Kobzar , V.D. Matveev , Y.D. Lapkin , R.R. Bogdanov , А. S. Izyumov

  2026-04-29

  Abstract ▼

  A large number of different interfaces can be used to control robots, from traditional remotes to augmented reality technologies. However, all such interfaces have a number of limitations, which are particularly acute in service robotics. They are associated with long-term training of a human operator, non-intuitive control for humans, and the need for full human involvement. On the other hand, a new direction has emerged today, related to large language models that are capable of processing natural language and then translating it into robot control commands. There are a number of works demonstrating the possibility of using language models in tasks of planning robot actions. Based on the analysis of existing work, a new method of controlling a mobile robot is proposed, combining the advantages of other methods. The method allows you to plan scenarios for the robot, receiving a natural language mission, the robot's TOP, and information from its sensors. The article also describes the sequence of configuring the system using a large language model to solve this problem. Three variants of instructions for the neural network are presented, which gradually improve the achievability of the generated scenarios. After that, various missions are described, which are set as part of experimental studies - a total of 100 missions were tested, divided into 4 levels of difficulty in equal proportions. The complexity of the missions ranged from describing objects in the robot's field of view to interacting with complex missions involving synonyms of objects and implicitly defined goals. At the end of the work, the results of the evaluation of the algorithm and three variants of the instruction are presented. The conclusion can be considered that the use of language models to assign scenarios to robots is possible, including with a sufficiently high achievement. The model with the most advanced instruction reached 91% of correctly formed scenarios, which suggests the applicability of the developed method for controlling a mobile robot in natural language

• A REVIEW OF METHODS FOR IMPROVING REASONING IN LARGE LANGUAGE MODELS

  V.B. Savinov , N.N. Shusharina

  2026-02-27

  Abstract ▼

  The emergence of large language models has become an important milestone in the field of natural language processing, as such models demonstrate impressive results in text generation, transformation, and analysis, as well as in solving a wide range of applied tasks. However, despite significant practical success, large language models possess limited reasoning capabilities. These limitations manifest in difficulties with generalizing knowledge beyond the training distribution, challenges in transferring knowledge to new contexts, and reduced accuracy when performing multi-step logical and mathematical operations. The goal of this work is to examine methods for improving the reasoning abilities of large language models, where reasoning is understood as the process of forming and evaluating inferences based on existing information. The paper discusses the main types of reasoning relevant to large language models: mathematical, logical, and commonsense reasoning. It provides a list of the most commonly used benchmarks applied to assess the reasoning quality of language models. An overview is presented of the methods used to enhance reasoning in large language models at 2025. Depending on the stage of application (during training or during model usage), the work examines approaches to training data preparation, architectural modifications of language models, training and finetuning procedures (including those using specially constructed synthetic datasets), reinforcement learning, various chain-of-thought construction techniques, mechanisms for integrating external tools, and multi-agent approaches. The paper also discusses existing limitations of large language models, which include the lack of conceptual understanding, poor out-of-distribution generalization, and reduced effectiveness as task complexity increases. Finally, the most promising methods aimed at improving the quality and reliability of reasoning in large language models are highlighted.